面向异构数据库集成的混合式元素分类技术研究

提出一种支持异构数据库集成的基于模式的元素分类技术,融合了基于语言和基于约束的分类方法,首先使用最大优先策略组合基于语言的分类算法形成复合元素名称分类器,然后将名称分类结果和元素模式约束转化为向量输入神经网络分类器,从而完成数据库元素分类.通过组合使用复合名称分类器和神经网络分类器,使得此方法可以应用于更复杂的分类环境.     

基于证据理论的不确定模式匹配方法

由于数据源数据模式的自治性、异构性，不确定性是模式匹配过程固有的本质特性。提出了一种基于证据理论的不确定性匹配方法，首先根据属性类型把模式空间分成若干模式子空间；然后将不同的匹配器结果看作不同的证据源，利用不同的匹配器的结果生成了多个基本概率分配函数，采用改进的Dempster组合规则把多个匹配器结果自动组合，减少人工干预，并解决了不同的匹配器结果组合时证据间冲突的问题；最后利用Kuhn Munkres算法获取模式映射。实验结果表明了方法的可行性和有效性。     

融合预训练语言模型的成语完形填空算法

根据上下文语境选择恰当的成语,是自然语言处理领域的重要任务之一.现有的研究将成语完型填空任务看成是文本匹配问题,虽然预训练语言模型能够在文本匹配研究上取得较高的准确率,但也存在明显的缺陷:一方面,预训练语言模型作为特征提取器时,会丢失句子间相互信息;另一方面,预训练语言模型作为文本匹配器时,计算开销大,训练时间和推理时间较长.另外,上下文与候选成语之间的匹配是不对称的,会影响预训练语言模型发挥匹配器的效果.为了解决上述两个问题,利用参数共享的思想,提出了TALBERT-blank.TALBERT-blank是将成语选择从基于上下文的不对称匹配过程转换为填空与候选答案之间的对称匹配过程,将预训练语言模型同时作为特征提取器和文本匹配器,并对句向量作潜在语义匹配.这样可以减少参数量和内存的消耗,在保持准确度的情况下,提高了训练和推理速度,达到了轻量高效的效果.在CHID数据集上的实验结果表明:作为匹配器,TALBERT-blank相较于ALBERT,在保证准确率的情况下,更大限度地精简了模型的结构,计算时间进一步缩短54.35%.     

基于传感器的网格服务匹配问题的优化篇

在开放网格服务架构（OGSA）下网格服务匹配是当前研究的热点。通过在服务网格模型中引入监控作业运行时间的传感器，给出了基于运行时间权矩阵的网格作业一网格服务匹配的优化解，经过仿真研究其算法与其它同类算法相比可使服务匹配系统性能明显改善。本算法还给出了用户作业和服务资源之间能实现完备匹配的充分必要条件。     

轻量级报文分类器的设计及在LBS中的应用

提出了一个轻量级有限状态通用报文匹配器及其线性代价的匹配实现.该匹配器通过提供支持即插即用的软件插槽将数据分类和数据处理逻辑分开,实现数据向上层的传递和用户定制信息处理,从而实现高度的可扩展性.通过聚合匹配器,用一个轻量级有限状态自动报文分类器实现了对多种报文格式的自动识别和有效报文提取.用C++语言实现了相应的代码,...     

基于路径特征的复杂本体匹配

复杂本体匹配方法主要分为基于匹配模式的方法和基于机器学习的方法,前者需要人工制定启发式规则而后者易陷入局部最优解。针对上述问题,提出一种融合匹配模式和机器学习的复杂匹配方法。引入路径特征刻画本体中实例具有的性质,路径特征是匹配模式的具体化。在此基础上,使用一阶归纳学习器组合路径特征得到复杂的匹配结果。实验结果表明,该方法可以自动学习到本体间的复杂匹配,且与基于一阶归纳学习器的复杂匹配方法相比,能够有效缓解局部最优问题。     

一种基于OBDD图的事件复合匹配方法

基于内容的Pub／Sub系统的核心问题是基于内容的事件匹配．在现有的方法中，订阅者使用简单约束来匹配事件内容，难以支持事件复合匹配．针对此问题，提出新的匹配模型，扩展简单匹配方法为多语义匹配并引入时间约束变量，支持依据语义对事件采取不同的操作和离散事件的处理，增强了事件匹配表达能力．在此基础上，将OBDD图扩展为层次着色OBDD图，证明了图扩展的等价性，给出基于扩展ODBB图的复合匹配算法，分析并验证了该算法的有效性．     

SKM:一种基于模式结构和已有匹配知识的模式匹配模型

针对已有基于模式结构的模式匹配方法的局限性,提出了一种利用模式结构信息和已有匹配知识的模式匹配模型——SKM(schema and reused knowledge based matching model).在该模型中,借鉴神经网络元之间的影响过程实现语义匹配推理;通过重用已有匹配知识深入挖掘模式元素之间的深层语义关系;基于已有匹配知识自动缩减不确定阈值区之间来确定匹配阈值,有效减少人工干涉;给出了简单的确定模式元素之间匹配关系的方法;同时通过自适应式迭代模型,进一步挖掘求精已有匹配知识.实验结果表明,SKM模型切实可行.     

普适计算服务匹配技术研究

在普适计算环境下需要根据用户的需求提供合适的服务,这需要对用户请求的服务和环境提供的服务进行服务的匹配.而目前使用的基于语法的服务匹配和基于语义的服务匹配都不能很好地适应普适计算环境的高效、资源约束和上下文感知的要求.通过分析普适计算环境下主要的基于语法的和基于语义的服务匹配技术,给出普适计算环境下在高效、资源约束和上下文感知的特殊要求下服务匹配的解决方案,并就面向服务组合的服务匹配进行了分析.     

一种光电混合型专家系统专用机OEHCES

本文基于在传统计算机上难以开发实时高效专家系统的事实，提出了一种比较容易实现的光电混合型专家系统专用机ＯＥＨＣＥＳ。 ＯＥＨＣＥＳ主要由主控机，知识库及光匹配器组成，并利用一种改进的知识表示法和光匹配器，部分解决在传统计算机中知识匹配速度慢的问题。     

An evidential approach to query interface matching on the deep Web

Matching query interfaces is a crucial step in data integration across multiple Web databases. The problem is closely related to schema matching that typically exploits different features of schemas. Relying on a particular feature of schemas is not sufficient. We propose an evidential approach to combining multiple matchers using Dempster–Shafer theory of evidence. First, our approach views the match results of an individual matcher as a source of evidence that provides a level of confidence on the validity of each candidate attribute correspondence. Second, it combines multiple sources of evidence to get a combined mass function that represents the overall level of confidence, taking into account the match results of different matchers. Our combination mechanism does not require the use of weighing parameters, hence no setting and tuning of them is needed. Third, it selects the top k attribute correspondences of each source attribute from the target schema based on the combined mass function. Finally it uses some heuristics to resolve any conflicts between the attribute correspondences of different source attributes. Our experimental results show that our approach is highly accurate and effective.     

Graph matching by neural relaxation

We propose a new relaxation scheme for graph matching in computer vision. The main distinguishing feature of our approach is that matching is formulated as a process of eliminating unlikely candidates rather than finding the best match directly. Bayesian development leads to a robust algorithm which can be implemented in a fast and efficient manner on a neural network architecture. We illustrate the utility of the technique through comparisons with its conventional counterpart on simulated and real-world data.     

Multi-view scans alignment for 3D spherical mosaicing in large-scale unstructured environments

We are currently developing a vision-based system aiming to perform a fully automatic pipeline for in situ photorealistic three-dimensional (3D) modeling of previously unknown, complex and unstructured underground environments. Since in such environments navigation sensors are not reliable, our system embeds only passive (camera) and active (laser) 3D vision senors. Laser Range Finders are particularly well suited for generating dense 3D maps by aligning multiples scans acquired from different viewpoints. Nevertheless, nowadays Iteratively Closest Point (ICP)-based scan matching techniques rely on heavy human operator intervention during a post-processing step. Since a human operator cannot access the site, these techniques are not suitable in high-risk underground environments. This paper presents an automatic on-line scan matcher able to cope with the nowadays 3D laser scanners’ architecture and to process either intensity or depth data to align scans, providing robustness with respect to the capture device. The proposed implementation emphasizes the portability of our algorithm on either single or multi-core embedded platforms for on-line mosaicing onboard 3D scanning devices. The proposed approach addresses key issues for in situ 3D modeling in difficult-to-access and unstructured environments and solves for the 3D scan matching problem within an environment-independent solution. Several tests performed in two prehistoric caves illustrate the reliability of the proposed method.     

Using visualisation as a tool for model-based recognition

The aim of the work reported here is to build a useful toolset for 3D model-based vision on an SIMD parallel machine, the AMT DAP. Included in the toolset are facilities for model specification, manipulation and rendering using a ray-tracing approach as well as model recognition and validation using a geometrical-matching approach. In particular, an SIMD parallel version of a ray tracer and an SIMD parallel version of a bottom-up geometrical matcher are described. The ray tracer can render constructive solid geometry models and incorporates spatial subdivision of the scene. The matcher uses edge primitives recovered from scenes to match to model edges using local constraints and deals with spurious data using bin assignments. The overall toolset is illustrated by its use in closed-form testing and refinement, where the models, camera geometry and frame-to-frame motion in an image sequence generated by the ray tracer are known, but are checked and validated using geometrical matching, recognition and localisation.     

The integrated methodology of rough set theory and artificial neural network for business failure prediction

This paper proposes a hybrid intelligent system that predicts the failure of firms based on the past financial performance data, combining rough set approach and neural network. We can get reduced information table, which implies that the number of evaluation criteria such as financial ratios and qualitative variables is reduced with no information loss through rough set approach. And then, this reduced information is used to develop classification rules and train neural network to infer appropriate parameters. The rules developed by rough set analysis show the best prediction accuracy if a case does match any of the rules. The rationale of our hybrid system is using rules developed by rough sets for an object that matches any of the rules and neural network for one that does not match any of them. The effectiveness of our methodology was verified by experiments comparing traditional discriminant analysis and neural network approach with our hybrid approach. For the experiment, the financial data of 2400 Korean firms during the period 1994–1997 were selected, and for the validation, k-fold validation was used.     

Tuning the ensemble selection process of schema matchers

Schema matching is the task of providing correspondences between concepts describing the meaning of data in various heterogeneous, distributed data sources. It is recognized to be one of the basic operations required by the process of data and schema integration and its outcome serves in many tasks such as targeted content delivery and view integration. Schema matching research has been going on for more than 25 years now. An interesting research topic, that was largely left untouched involves the automatic selection of schema matchers to an ensemble, a set of schema matchers. To the best of our knowledge, none of the existing algorithmic solutions offer such a selection feature. In this paper we provide a thorough investigation of this research topic. We introduce a new heuristic, Schema Matcher Boosting (SMB). We show that SMB has the ability to choose among schema matchers and to tune their importance. As such, SMB introduces a new promise for schema matcher designers. Instead of trying to design a perfect schema matcher, a designer can instead focus on finding better than random schema matchers. For the effective utilization of SMB, we propose a complementary approach to the design of new schema matchers. We separate schema matchers into first-line and second-line matchers. First-line schema matchers were designed by-and-large as applications of existing works in other areas (e.g., machine learning and information retrieval) to schemata. Second-line schema matchers operate on the outcome of other schema matchers to improve their original outcome. SMB selects matcher pairs, where each pair contains a first-line matcher and a second-line matcher. We run a thorough set of experiments to analyze SMB ability to effectively choose schema matchers and show that SMB performs better than other, state-of-the-art ensemble matchers.     

基于BP神经网络的属性匹配方法研究

为了实现异构数据库的数据共享，关键的问题就是要找出数据库间的相同属性。目前主要采用的方法是通过比较所有的属性来实现属性的相似性匹配，但是当同一属性用不同数据类型表示时，由于描述属性的元数据信息和取值信息的极大差异性，这些方法就不能找出相同的属性。并且将不同数据类型描述的属性放在一起匹配，还会造成属性数据之间的干扰，影响匹配结果的准确性。为此，本文提出一种基于BP神经网络的二步检查法属性匹配算法。该算法中属性首先根据数据类型进行分类，然后用分类后的属性集分别多次训练神经网络，并对每次的匹配结果求交集作为最终的属性匹配结果，进行两阶段检查，即二步检查法。该算法能有效地消除不一致信息的干扰，降低神经网络的规模，并且可以实现不同数据类型的属性集之间属性匹配过程的并行计算。实验结果显示本文提出的方法能明显地提高系统的运行效率、属性匹配的查准率和查全率。     

模式匹配方法研究*

从模式匹配的定义开始介绍,对已有的模式匹配方法进行分类,探讨了这些方法适用的领域和所能发掘的信息,区分了实例级和模式级、元素级和结构级以及基于自然语言和基于约束的匹配程序,以期在比较不同模式匹配方法或研究新匹配算法以及实现模式匹配组件时有所帮助。     

基于模糊逻辑的多相机非重叠场景的物体匹配

An approach based on fuzzy logic for matching both articulated and non-articulated objects across multiple non-overlapping field of views (FoVs) from multiple cameras is proposed. We call it fuzzy logic matching algorithm (FLMA). The approach uses the information of object motion, shape and camera topology for matching objects across camera views. The motion and shape information of targets are obtained by tracking them using a combination of ConDensation and CAMShift tracking algorithms. The information of camera topology is obtained and used by calculating the projective transformation of each view with the common ground plane. The algorithm is suitable for tracking non-rigid objects with both linear and non-linear motion. We show videos of tracking objects across multiple cameras based on FLMA. From our experiments, the system is able to correctly match the targets across views with a high accuracy.     

High efficiency and quality: large graphs matching

Graph matching plays an essential role in many real applications. In this paper, we study how to match two large graphs by maximizing the number of matched edges, which is known as maximum common subgraph matching and is NP-hard. To find exact matching, it cannot a graph with more than 30 nodes. To find an approximate matching, the quality can be very poor. We propose a novel two-step approach that can efficiently match two large graphs over thousands of nodes with high matching quality. In the first step, we propose an anchor-selection/expansion approach to compute a good initial matching. In the second step, we propose a new approach to refine the initial matching. We give the optimality of our refinement and discuss how to randomly refine the matching with different combinations. We further show how to extend our solution to handle labeled graphs. We conducted extensive testing using real and synthetic datasets and report our findings in this paper.